用自监督元学习让神经网络全程在线自适应，实现稳定飞行控制
Self-Supervised Meta-Learning for All-Layer DNN-Based Adaptive Control with Stability Guarantees
- 离线用轨迹时间一致性自监督训练神经网络预测干扰
- 在线全参数更新神经网络，性能比基线提升19%-39%
- 适合需要快速适应动态环境的机器人控制场景
自适应控制的核心目标是使机器人在动态环境中快速适应。现有基于元学习的自适应控制方法利用元学习从离线数据中提取非线性特征（由深度神经网络表示），并通过自适应控制在线更新线性系数。然而，该方法受限于不确定性的线性参数化，未能充分发挥深度神经网络的能力。本文提出一种新型学习型自适应控制框架：通过离线自监督元学习（SSML）预训练深度神经网络，并在在线阶段通过复合自适应方法更新整个神经网络。离线阶段利用轨迹数据的时间一致性，以无监督方式训练神经网络根据历史信息预测未来扰动；在线阶段设计控制律与自适应律，确保全网络参数更新时具有稳定性保证。实验表明，在强风扰动下的真实四旋翼跟踪任务中，该框架显著优于多种经典及学习型自适应控制基线，性能提升达19%-39%。
原文摘要 · Abstract (English)
A critical goal of adaptive control is enabling robots to rapidly adapt in dynamic environments. Recent studies have developed a meta-learning-based adaptive control scheme, which uses meta-learning to extract nonlinear features (represented by Deep Neural Networks (DNNs)) from offline data, and uses adaptive control to update linear coefficients online. However, such a scheme is fundamentally limited by the linear parameterization of uncertainties and does not fully unleash the capability of DNNs. This paper introduces a novel learning-based adaptive control framework that pretrains a DNN via self-supervised meta-learning (SSML) from offline trajectories and online adapts the full DNN via composite adaptation. In particular, the offline SSML stage leverages the time consistency in trajectory data to train the DNN to predict future disturbances from history, in a self-supervised manner without environment condition labels. The online stage carefully designs a control law and an adaptation law to update the full DNN with stability guarantees. Empirically, the proposed framework significantly outperforms (19-39%) various classic and learning-based adaptive control baselines, in challenging real-world quadrotor tracking problems under large dynamic wind disturbance.
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